2014/12/01 by Vadim Zipunnikov, Sonja Greven, Haochang Shou +4 · 1 citation
Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Fetal and Pediatric Neurological Disorders #Multiple Sclerosis Research Studies #stat.AP
paper · pdf · doi:10.1214/14-aoas748
published as Annals of Applied Statistics 2014, Vol. 8, No. 4, 2175-2202 · Published in at http://dx.doi.org/10.1214/14-AOAS748 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2014/12/01 · arxiv created 2015/01/19 · arxiv updated 2015/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We develop a flexible framework for modeling high-dimensional imaging data observed longitudinally. The approach decomposes the observed variability of repeatedly measured high-dimensional observations into three additive components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the dynamic irreversible deformation over multiple realizations, and a subject-visit specific imaging deviation that quantifies exchangeable effects between visits. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis of diffusion tensor imaging (DTI) data of the corpus callosum of multiple sclerosis (MS) subjects. The study includes 176 subjects observed at 466 visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 30,000 voxels.